Skip navigation
SuUB logo
DSpace logo

  • Home
  • Institutions
    • University of Bremen
    • City University of Applied Sciences
    • Bremerhaven University of Applied Sciences
  • Sign on to:
    • My Media
    • Receive email
      updates
    • Edit Account details

Citation link: https://doi.org/10.26092/elib/2704
deep_learning_for_ct_reconstruction_phd_thesis_leuschner.pdf
OpenAccess
 
by 4.0

Deep learning for computed tomography reconstruction - learned methods, deep image prior and uncertainty estimation


File Description SizeFormat
deep_learning_for_ct_reconstruction_phd_thesis_leuschner.pdf35.61 MBAdobe PDFView/Open
Authors: Leuschner, Johannes  
Supervisor: Maaß, Peter  
Jin, Bangti  
1. Expert: Maaß, Peter  
Experts: BUBBA, Tatiana Alessandra  
Abstract: 
X-ray computed tomography (CT) is a highly relevant imaging technique with clinical and industrial applications. At its core, CT involves an image reconstruction task from detector measurements that are acquired from multiple projection angles. Improving CT reconstruction using deep learning, which is being explored and utilized in various fields, is a subject of recent and current research.
This thesis comprises six papers, whose contributions can be summarized as two-fold. First, several deep learning approaches are compared quantitatively and qualitatively, involving the creation of a benchmark dataset as well as the realization and evaluation of challenges for learned low-dose and sparse-view CT reconstruction. Second, several extensions of the deep image prior (DIP)—an unsupervised deep learning image reconstruction framework—are investigated. This includes its application to CT using total-variation regularization, pretraining on synthetically generated data, and uncertainty estimation via a probabilistic model. These extensions benefit DIP-based CT reconstruction in several ways, such as an improved reconstruction quality, an accelerated reconstruction process, and the identification of potential errors in the reconstruction. Additionally, a Bayesian experimental design approach utilizing the uncertainty estimation is studied for the selection of scanning angles based on a pilot scan.
Complementing the papers, which are included without any modifications in the second part of this thesis, the first part introduces relevant foundations, as well as a large overview of literature on deep learning for CT reconstruction.
Keywords: deep learning; computed tomography; comparison of methods; Deep Image Prior; image reconstruction; uncertainty
Issue Date: 30-Nov-2023
Type: Dissertation
DOI: 10.26092/elib/2704
URN: urn:nbn:de:gbv:46-elib74810
Institution: Universität Bremen 
Faculty: Fachbereich 03: Mathematik/Informatik (FB 03) 
Appears in Collections:Dissertationen

  

Page view(s)

399
checked on May 11, 2025

Download(s)

515
checked on May 11, 2025

Google ScholarTM

Check


This item is licensed under a Creative Commons License Creative Commons

Legal notice -Feedback -Data privacy
Media - Extension maintained and optimized by Logo 4SCIENCE